Algorithms and estimators for summarization of unaggregated data streams

Edith Cohen; Nick Duffield; Haim Kaplan; Carstent Lund; Mikkel Thorup

doi:10.1016/j.jcss.2014.04.009

Algorithms and estimators for summarization of unaggregated data streams

Edith Cohen, Nick Duffield, Haim Kaplan, Carstent Lund, Mikkel Thorup

Datalogisk Institut

10 Citationer (Scopus)

Abstract

Abstract Statistical summaries of IP traffic are at the heart of network operation and are used to recover aggregate information on subpopulations of flows. It is therefore of great importance to collect the most accurate and informative summaries given the router's resource constraints. A summarization algorithm, such as Cisco's sampled NetFlow, is applied to IP packet streams that consist of multiple interleaving IP flows. We develop sampling algorithms and unbiased estimators which address sources of inefficiency in current methods. First, we design tunable algorithms whereas currently a single parameter (the sampling rate) controls utilization of both memory and processing/access speed (which means that it has to be set according to the bottleneck resource). Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during the measurement period.

Originalsprog	Dansk
Tidsskrift	Journal of Computer and System Sciences
Vol/bind	80
Udgave nummer	7
Sider (fra-til)	1214-1244
Antal sider	31
ISSN	0022-0000
DOI	https://doi.org/10.1016/j.jcss.2014.04.009
Status	Udgivet - nov. 2014

Emneord

NetFlow
Data streams
Random sampling
IP flows
Subpopulation queries
Flow size distribution

Adgang til dokumentet

10.1016/j.jcss.2014.04.009

Citationsformater

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AU - Cohen, Edith

AU - Duffield, Nick

AU - Kaplan, Haim

AU - Lund, Carstent

AU - Thorup, Mikkel

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N2 - Abstract Statistical summaries of IP traffic are at the heart of network operation and are used to recover aggregate information on subpopulations of flows. It is therefore of great importance to collect the most accurate and informative summaries given the router's resource constraints. A summarization algorithm, such as Cisco's sampled NetFlow, is applied to IP packet streams that consist of multiple interleaving IP flows. We develop sampling algorithms and unbiased estimators which address sources of inefficiency in current methods. First, we design tunable algorithms whereas currently a single parameter (the sampling rate) controls utilization of both memory and processing/access speed (which means that it has to be set according to the bottleneck resource). Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during the measurement period.

AB - Abstract Statistical summaries of IP traffic are at the heart of network operation and are used to recover aggregate information on subpopulations of flows. It is therefore of great importance to collect the most accurate and informative summaries given the router's resource constraints. A summarization algorithm, such as Cisco's sampled NetFlow, is applied to IP packet streams that consist of multiple interleaving IP flows. We develop sampling algorithms and unbiased estimators which address sources of inefficiency in current methods. First, we design tunable algorithms whereas currently a single parameter (the sampling rate) controls utilization of both memory and processing/access speed (which means that it has to be set according to the bottleneck resource). Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during the measurement period.

KW - NetFlow

KW - Data streams

KW - Random sampling

KW - IP flows

KW - Subpopulation queries

KW - Flow size distribution

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DO - 10.1016/j.jcss.2014.04.009

M3 - Tidsskriftartikel

SN - 0022-0000

VL - 80

SP - 1214

EP - 1244

JO - Journal of Computer and System Sciences

JF - Journal of Computer and System Sciences

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